Healthcare News & Insights

Control ED wait times with predictive analytics

Reducing wait times and overcrowding in the emergency department (ED) is a top priority for most hospitals. But it can be difficult to pinpoint the exact issues that are causing bottlenecks. New research gives hospitals a data-based framework so they can easily identify problems. 

The research, from Columbia UniversityEmergencySign, discusses the difficulties hospitals have with controlling overcrowding in the ED. According to statistics from the American Hospital Association (AHA) that are cited in the research, nearly half of hospitals (47%) are regularly at or over capacity for patients in the ED.

While many hospitals have addressed the issue by increasing bed capacity or adding extra staff to busy shifts, there’s an alternative that may be more cost-effective in the long run.

Hospitals can use predictive analytics, a practice that’s been growing in popularity since several vendors and solutions have made data analysis more accessible in health care, particularly in regards to value-based payments.

How data analysis helps

With predictive analytics, facilities can use an algorithm or formula that breaks down raw data to get a more accurate idea of how many patients will typically visit the ED on any given day.

The algorithm used by Columbia takes several factors into account, including the times of the year when hospitals receive the most ED traffic.

Similar estimates have been used to staff shifts, but the Columbia research aims to give hospitals data to not only appropriately staff the ED, but to improve efficiency and performance so more patients can be seen in a shorter time frame.

Unlike other methods of estimating ED traffic that rely on discharges, the Columbia research uses information about patient arrivals instead. Researchers suggest that hospitals should look at admissions control as a first step toward managing wait times during peak times of ED use.

In a nutshell, the algorithm uses details about the average flow of patients through an ED so staff can determine how many patients they can admit at any given point to keep their average wait time at a reasonable level.

Target diversion

If an ED expects to receive more patients than that number, for whatever reason, the research says that the hospital should practice targeted diversion, proactively sending patients to different facilities. Using predictive analytics, hospitals can keep the number of diversions at a bare minimum.

Targeted diversion helps preserve the bottom line, enabling hospitals to see as many patients in the ED as staff can feasibly treat, without subjecting people to extremely long waits and overcrowding.

The research goes into depth about the manual algorithm and its applications, but hospitals can work with IT to find various technology solutions geared toward data analytics in hospitals. These solutions can help with interpreting data and applying custom formulas and algorithms to it.

Whether they work with outside vendors or hire in-house data specialists, facilities will likely earn a significant return on any investments they make for using admissions data to improve patient flow in the ED.

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